Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73610278" target="_blank" >RIV/61989592:15310/22:73610278 - isvavai.cz</a>
Result on the web
<a href="https://obd.upol.cz/id_publ/333190165" target="_blank" >https://obd.upol.cz/id_publ/333190165</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/14690667211066725" target="_blank" >10.1177/14690667211066725</a>
Alternative languages
Result language
angličtina
Original language name
Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library
Original language description
Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10608 - Biochemistry and molecular biology
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
EUROPEAN JOURNAL OF MASS SPECTROMETRY
ISSN
1469-0667
e-ISSN
1751-6838
Volume of the periodical
27
Issue of the periodical within the volume
6
Country of publishing house
GB - UNITED KINGDOM
Number of pages
18
Pages from-to
"217 "- 234
UT code for WoS article
000740966800001
EID of the result in the Scopus database
2-s2.0-85122400318